Some recent seminars, workshops and conferences

19–23 June 2017: I co–organazied and participated in a joint working seminar on the ζ–function and the Stieltjes constants γm with
Joseph Oesterlé and
Yuri Matiyasevich.
Five days of a very interesting mathematical exchange with two great Mathematicians at the Steklov Institute of Mathematics at St. Petersburg, Russia.

5 October 2016:
I participated in a seminar helded by Stanislav Smirnov
(Fields Medal, 2010) devoted to some applications of the mathematical physics to the petroleum engineering. Mathematics and Mechanics Faculty, St. Petersburg State University, Russia.

Title

Speech modeling and processing. Control of a Speech Robot via an Optimum Neural–Network–Based Internal Model with Constraints.
Statistical tools in speech processing.

Abstract

This Ph.D. dissertation deals with speech modeling and processing, which both share the speech quality aspect.
An optimum internal model with constraints is proposed and
discussed for the control of a biomechanical speech robot based on the equilibrium point
hypothesis (EPH, λ–model). It is supposed that the robot internal space is composed of the
motor commands λ of the equilibrium point hypothesis. The main idea of the work is that
the robot movements, and in particular the robot speech production, are carried out in such a
way that, the length of the path traveled in the internal space, is minimized, under acoustical
and mechanical constraints. Mathematical aspect of the problem leads to one of the problems
of variational calculus, the so–called geodesic problem, whose exact analytical solution is quite
complicated. By using some empirical findings, an approximate solution for the proposed optimum
internal model is then developed and implemented. It gives interesting and challenging
results, and shows that the proposed internal model is quite realistic; namely, some similarities
are found between the robot speech and the real one. Next, by aiming to analyze speech signal,
several methods of statistical speech signal processing are developed. They are based on higher–order statistics
(namely, on normalized central moments and on the fourth–order cumulant), as
well as on discrete normalized entropy. In this framework, we also designed an unbiased and
efficient estimator of the fourth–order cumulant in both batch and adaptive versions.